Decision Support Tool for IoT Service Providers: Cost-Performance Optimization for IoT-based Sensor-Actor Systems

학위논문 (박사)-- 서울대학교 대학원 : 기술경영·경제·정책전공, 2017. 2. 황준석.The Internet of Things (IoT) refers to the uniquely identifiable objects (things) and their virtual representations in an Internet-like structure. IoT has appeared on the stage, interconnecting a variety of physical objects over the Internet, enabling the objects to communicate and cooperate with each other to achieve predefined goals. It is predicted to be an integral component of the Future Internet (FI)therefore, IoT should be seamlessly and smoothly integrated with other FI integrated services. Nevertheless, IoT devices are location dependent, and expensive to develop and deploy, because the IoT supporting infrastructure such as computing power, storage and networks are resource constrained. To fulfill these shortcomings, another recent phenomenon named Cloud computing can be the most promising and cost-effective solution. Indeed, Cloud offers relatively cheap, ubiquitous, unlimited and elastic solution for the supporting infrastructure. Therefore, to connect, manage and track the IoT-based devices and provide feasible access to a set of multitude computing resources, many IoT Service Providers (IoTSP) may utilize Clouds to offer their users certain services. Aiming to offer services to globally distributed users, an IoTSP will deploy its Virtual Machines (VMs) on multi Clouds that is consisting of various Cloud Service Providers (CSPs) to have satisfactory coverage and performance for the users. Integration of IoT with the multi Clouds raises new challenges among which the economic challenges are from the most critical factors for success of the IoTSP business. One of the major problems in this context is to minimize the overall cost of the IoT system while keeping satisfactory level of performance in order for the business to be profitable. To this end, the IoTSP has to maintain its IoT-devices cost-optimally and to place its VMs on the available CSPs cost-optimally. In other words, the problem for IoTSPs is finding the cost optimum placement for their IoT devices and VMs. This dissertation addresses the problem by proposing a decision support tool for IoTSPs to find their cost-optimum devices and VM placement on multi Clouds. Sound system architecture is designed for the tool to carry out the tasks. The tool comprises a heuristic algorithm for IoT device placement, a cost estimation module as well as VM placement optimization algorithm. The cost estimation module estimates the total infrastructure costs for any given VM placements considering multi Cloud environment. Applying the proposed optimization algorithms on the estimated cost and the expected performance returns the cost-optimum IoT device and VM placement for the IoTSP. The proposed decision support tool is examined by several simulation scenarios and the results demonstrate the working of the tool.Chapter 1. Introduction 1 1.1 Introduction and Background 1 1.2 Problem Description 5 1.3 Research Objectives 12 1.4 Research Questions 13 1.5 Methodology 14 1.6 Contribution of the Research 14 1.7 Research Outline 16 Chapter 2. Literature Review 18 2.1 Internet of Things: IoT 18 2.2 Cloud Computing 20 2.3 CloudIoT: Integration of IoT and Clouds 24 2.4 CloudIoT Challenges and Issues 26 2.5 Resource Allocation 29 Chapter 3. Methodology 43 3.1 The proposed Decision Support Tool 43 3.2 Chapter Summary 69 Chapter 4. Simulation 70 4.1 Suggested IoT-based Danger Recovery Scenarios 73 4.1.1 Cardiac Arrest/Heart Attack 74 4.1.2 Fire Extinguisher 76 4.2 Actor Placement Optimization Simulation 78 4.2.1 Assumptions and Simulation Configurations 79 4.2.2 Actor-Placement Simulation Scenarios 80 4.2.3 IoT-based Healthcare 80 4.2.4 IoT-based Disaster Recovery 83 4.3 VM Placement Optimization Simulation 84 4.3.1 Assumptions and Simulation Configurations 84 4.3.2 VM-Placement Simulation Scenarios 88 4.4 Chapter summary 92 Chapter 5. Experiments and Results 93 5.1 Actor Placement Optimization Simulation 93 5.1.1 IoT-based Healthcare 94 5.1.2 IoT-based Disaster Recovery 119 5.2 VM Placement Optimization Simulation 133 5.2.1 Single Powerful Data Center 133 5.2.2 Two Small Size Data Centers 134 5.2.3 Two Powerful Data Centers 136 5.2.4 Full Data Center Coverage 138 5.3 Discussion 139 Chapter 6. Conclusion 143 Chapter 7. Bibliography 149 Appendixes 162 국문초록 226Docto